Cardiovascular diseases are one of the leading causes of death in United States. The development of new technologies to diagnose cardiovascular diseases has led to a decline of the mortality rate. Magnetic resonance imaging (“MRI”) provides time varying three dimensional imagery of the heart that can be processed using Computer Vision techniques. The three dimensional imagery can then be used for diagnostic purposes. Therefore, physicians are interested in identifying heart chambers using three dimensional imagery so that this information can be used for diagnostic purposes.
In medical applications the areas of interest in the image domain are actual anatomic structures. The segmentation result is constrained according to some a priori high level knowledge of an anatomic structure, such as, forms, relative positions, and motion over time.
The extraction of accurate results is a requirement in medical applications. Therefore, predominately boundary-based methods, enforced by some region-based segmentation modules, have been employed to cope with the segmentation task. These methods can be classified into two categories. The first category is parametric methods that determine the segmentation map by fitting a boundary template to the image. The second category is non-parametric methods that are based on the propagation of regular curves under the influence of local image characteristics.
Parametric methods have real-time performance and can deal with incomplete data and slight deformations due to shape-driven constraints. However, such methods primarily refer to boundary information and require complicated models to deal with important shape deformations while topological changes cannot be handled.
Non-Parametric methods can deal with important shape deformations and topological changes. At the same time these methods do not require a priori knowledge. However, non-parametric methods do not have a robust behavior due to the presence of noise and are time consuming.
Identifying heart chambers, in particular, the endocardium and the epicardium, is a challenging problem in medical image analysis. Furthermore, measuring ventricular blood volume, ventricular wall mass, ventricular wall motion and wall thickening properties over various stages of a cardiac cycle are powerful diagnostic tools that are studied in medical image analysis. The left ventricle is of particular interest because it pumps oxygenated blood out of the heart to distant tissue in the entire body. The information space refers to physically corrupted data. Small parts, for example, papillary muscles, of an endocardium do not have the same intensity properties as its dominant parts. At the same time, the separation, that is, boundaries, of the epicardium and the rest of the heart is practically impossible to detect in some areas, due to lack of information.
A need exists for a unified mathematical framework that makes optimal use of visual information, translates high level application constraints into low level segmentation modules, is able to deal with lack of visual information, and is able to deal with physically corrupted data.